Abstract
Fake news spreading through social media has become a serious problem in recent years, especially after the United States presidential election in 2016. Accordingly, more attention has been paid to this issue by scientists to develop automated tools to combat those pieces of information that contain misinformation, using natural language processing methods. Although the performance of fake news detection models has increased by using more complex architectures and state-of-the-art models, less attention has been paid to the impact of pre-processing on the overall performance of such models. In this study, we focus on investigating the impact of pre-processing, especially removing URLs on the performance of fake news detection systems. We compared the performance of fake news detection in tweets as a text classification task, using support vector machine, long short-term memory networks, and BERT pre-trained model. In addition to URLs, we analyzed the impact of different approaches for dealing with emojis and Twitter handles on the performance of the models. Our results show URLs could be good clues for identifying fake news, despite the fact that they are usually removed in pre-processing step.
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More details about the implementation and parameters can be found in the GitHub repository of the project at https://github.com/salarmohtaj/FakeNews_Detection_Twitter.
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Acknowledgment
This research was funded in part by the German Federal Ministry of Education and Research (BMBF) under grant number 01IS17043 (project ILSFAS).
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Mohtaj, S., Möller, S. (2022). The Impact of Pre-processing on the Performance of Automated Fake News Detection. In: Barrón-Cedeño, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2022. Lecture Notes in Computer Science, vol 13390. Springer, Cham. https://doi.org/10.1007/978-3-031-13643-6_7
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